待翻譯：Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators

AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。

--> [Submitted on 7 Aug 2026] Title:Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators View a PDF of the paper titled Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators, by Pierre Nodet and Thomas George View PDF HTML (experimental) Abstract:We adapt two classical statistical estimators for quantifying uncertainty to modern deep learning, in order to provide clearer insights into uncertainty attributable to two sources : aleatoric uncertainty, or locally scarce data. Our approach leverages recent advances in approximate Fisher Information Matrices, to enable scaling to actual architectures. Experimental results demonstrate how each test points is differentially impacted by both sources, highlighting the practical utility of our estimators in improving the robustness of real-world applications. Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2608.07630 [cs.LG] (or arXiv:2608.07630v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.07630 arXiv-issued DOI via DataCite Submission history From: Thomas George [view email] [v1] Fri, 7 Aug 2026 12:18:52 UTC (9,181 KB) Full-text links: Access Paper: View a PDF of the paper titled Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators, by Pierre Nodet and Thomas George View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG new | recent | 2026-08 Change to browse by: cs stat stat.ML References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)